A typical contact center algorithmically assigns contacts arriving at the contact center to agents available to handle those contacts. Several potential algorithms exist for assigning contacts to contact center agents. These include time-ordered assignment strategies, utilization-based assignment strategies, performance-based assignment strategies, and behaviorally-based assignment strategies.
At times, contact center administrators may wish to compare the performance of one algorithm against another. In some cases, contact center administrators can do this by alternating between the two algorithms and examining the resultant differences in performance over time. Such a benchmarking process can be subject to the Yule-Simpson effect (also referred to as “Simpson's Paradox”) in which the aggregation or amalgamation of distinct cross-sections of data can result in a misleading assessment of the actual performance differential between the assignment algorithms being alternated.
In some cases, such a mischaracterization of performance can be large. For example, one algorithm may consistently outperform another in each of the periods in which it was responsible for contact assignment, but when aggregated the apparent performance of the two algorithms may in fact be reversed.
In view of the foregoing, it may be understood that there is a need for a system that corrects for such a mischaracterization that can result from the Yule-Simpson effect.